Indigenous schools and a decolonial model of education: sharing experiences between Canada and Brazil
Bibliographic record
Abstract
There are more similarities between Brazil and Canada than some people would believe. First Nations have established. Both in Brazil and Canada, the same opportunity to develop their own schools and manage most of them by the community and for the community. However, the government has a crucial role in this play of schooling processes in First Nations communities in Brazil and Canada. The idea of submitting all the children to the same structure of schooling based in western pedagogies and curriculum does not represent the holistic perspective of First Nations knowledge and pedagogy. The holistic perspective embraces the First Nations knowledge attached to land, language, spirituality, and many other aspects of life that do not fit inside a western schooling process. These foundational differences are substantial and as a result this dissertation argues that we need an alternative schooling process to congregate the two ways of seeing the world. This dissertation focuses on schooling processes inside First Nations schools in Brazil and Canada, which follow the same western-capitalistic-pedagogy. The school should be the centre of recognition of differences, and not a Government instrument of unification and universalization of the subject’s individualities. We do believe it is time to create and think an autonomous and independent first Nation Schooling Process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".